def test_gate_error(self):
gatedConnections = dict()
# One target neuron.
gatedConnections[self.targetNeuron] = [self.g1]
error = functions.calculateGateError(self.derivative, gatedConnections)
self.assertAlmostEqual(67.5, error)
# Two target neuron.
# Second neuron has multiple connections.
gatedConnections[self.targetNeuron2] = [self.g2, self.g3]
error = functions.calculateGateError(self.derivative, gatedConnections)
self.assertAlmostEqual(67.5 + 80 + 240, error)
def backpropagate(self, source):
"""
Performs the backpropagation of error through this neuron.
This method will collect all of the errors from its output neurons
and gated neurons before calculating performing weight adjustments.
"""
# Check off source.
try: self.outputSet.remove(source)
except KeyError: pass
# Only continue if we have been notified by all output neurons.
if len(self.outputSet) == 0:
# Reset output set.
self.resetOutputSet()
## Projected error.
self.projectedError = \
calculateProjectedError(self.derivative, self.projectedConnections)
## Gate error.
gateError = calculateGateError(self.derivative, self.gatedConnections)
# Calculate total error.
self.error = gateError + self.projectedError
# Backpropagate.
for neuron in self.inputSet: neuron.backpropagate(self)
